from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

ext = 'drive/MyDrive/CV_Images/'#concentric.mat'

! pip install imagehash

Collecting imagehash
  Downloading ImageHash-4.2.1.tar.gz (812 kB)
     |████████████████████████████████| 812 kB 14.2 MB/s 
Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imagehash) (1.15.0)
Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from imagehash) (1.21.6)
Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from imagehash) (1.4.1)
Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from imagehash) (7.1.2)
Requirement already satisfied: PyWavelets in /usr/local/lib/python3.7/dist-packages (from imagehash) (1.3.0)
Building wheels for collected packages: imagehash
  Building wheel for imagehash (setup.py) ... done
  Created wheel for imagehash: filename=ImageHash-4.2.1-py2.py3-none-any.whl size=295206 sha256=cb3b6dc6eb3f76ad5f77bdc5ff3c940c9ef56a0b9b3175b027303dd0e5f22381
  Stored in directory: /root/.cache/pip/wheels/4c/d5/59/5e3e297533ddb09407769762985d134135064c6831e29a914e
Successfully built imagehash
Installing collected packages: imagehash
Successfully installed imagehash-4.2.1

import cv2
import numpy as np
import json
import os
import random
import tensorflow as tf
from keras.utils import np_utils
from keras.models import Model, Sequential, load_model
from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Reshape, Dropout
from keras.preprocessing.image import ImageDataGenerator
from keras.applications.vgg16 import VGG16
#from keras.applications.resnet50 import ResNet50
from keras.applications.resnet import ResNet50
from sklearn.metrics import confusion_matrix, classification_report
import itertools
import pandas as pd
import matplotlib.pyplot as plt
from keras.callbacks import EarlyStopping
import seaborn as sns
import shutil
from PIL import Image
from matplotlib import image
from sklearn.cluster import AgglomerativeClustering
from tensorflow.keras.preprocessing.image import ImageDataGenerator

Creating Data loader

#path = '/content/drive/MyDrive/CV_Images'
path = '/content/drive/MyDrive/Computer vision/CV_Images'

def data_loader(path, data_name, img_size): # data_name = 'cropped_train', # img_size = 128 
  all_images = []
  all_files = ['1040','1019', '1015', '1004', '1005','1039','1017','1021']#,'1018'] #,'1008' #,'1021'] # ['1015','1019'] # for now manually changed as issue in 1021, 1040 #'1004', '1005','1019',
  for label_folder in all_files:
    inside_path = path+'/'+ label_folder +'/'+ data_name
    fold = os.listdir(inside_path)
    for img  in fold:
      ext = os.path.splitext(img)[-1]
      if ext in ['.jpg', '.JPG']: # only jpg files no json files
        try:
          img_arr = cv2.imread(os.path.join(inside_path, img))
          #print(img_arr.shape)
          new_arr = cv2.resize(img_arr, (img_size, img_size)) 
          all_images.append([new_arr, label_folder])  
        except:
          pass 
    print(label_folder,data_name,len(all_images))
    random.shuffle(all_images)
  return all_images


# rename cropped_train to folder name which you want to run
img_train = data_loader(path, 'train', 224)
img_test_cr = data_loader(path, 'cropped_test', 224)
img_test_uncr = data_loader(path, 'uncropped_test', 224)

1040 train 37
1019 train 103
1015 train 208
1004 train 283
1005 train 345
1039 train 419
1017 train 497
1021 train 641
1040 cropped_test 12
1019 cropped_test 64
1015 cropped_test 81
1004 cropped_test 119
1005 cropped_test 162
1039 cropped_test 200
1017 cropped_test 221
1021 cropped_test 232
1040 uncropped_test 12
1019 uncropped_test 64
1015 uncropped_test 81
1004 uncropped_test 119
1005 uncropped_test 162
1039 uncropped_test 200
1017 uncropped_test 221
1021 uncropped_test 232

Preprocesesing the data

label_dict = {'1040':0, '1019':1, '1015':2, '1004':3, '1005':4, 
              '1039':5, '1017':6, '1021':7}#, '1018':8}  #'1008':8, # add the label in the dictionary #'1004':0, '1005':1, 



classes  = 8#7 #6 #5 #2 # 4
def preprocesing(data, num_class):
  X, y = [], []
  for features, label in data:
    X.append(features)                 
    y.append(label)  
  X = np.array(X).reshape(-1, 224, 224, 3)
  encoding =np.vectorize(label_dict.get)(y) # label encoding
  y = np.array(encoding)
  y = tf.keras.utils.to_categorical(y, num_classes=num_class) # one hot encoding
  print("The shape of the dataset:", X.shape)
  print("The shape of the label dataset:", y.shape)
  #X = X/255
  return X, y

X_train, y_train = preprocesing(img_train,classes)
X_test_cr, y_test_cr = preprocesing(img_test_cr,classes)
X_test_uncr, y_test_uncr = preprocesing(img_test_uncr,classes)

The shape of the dataset: (641, 224, 224, 3)
The shape of the label dataset: (641, 8)
The shape of the dataset: (232, 224, 224, 3)
The shape of the label dataset: (232, 8)
The shape of the dataset: (232, 224, 224, 3)
The shape of the label dataset: (232, 8)

VGG-16

from keras.applications.vgg16 import preprocess_input
X_train = preprocess_input(X_train)
X_test_cr = preprocess_input(X_test_cr)
X_test_uncr = preprocess_input(X_test_uncr)

Data Augmentation

X_train_dg = ImageDataGenerator()
X_train_dg.fit(X_train)

Install this addon to run discriminative learning rate

!pip install tensorflow-addons
import tensorflow_addons as tfa
from tensorflow.keras.optimizers import Adam

Collecting tensorflow-addons
  Downloading tensorflow_addons-0.16.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)
     |████████████████████████████████| 1.1 MB 4.1 MB/s 
Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow-addons) (2.7.1)
Installing collected packages: tensorflow-addons
Successfully installed tensorflow-addons-0.16.1

Model Architechture

He_initializer = tf.keras.initializers.HeNormal()
def getVGG16Model(lastFourTrainable=False):
  vgg_model = VGG16(weights='imagenet', input_shape=X_train[0].shape, include_top=True)

  # Make all layers untrainable
  for layer in vgg_model.layers[:]:
      layer.trainable = False

  # Add fully connected layer which have 1024 neuron to VGG-16 model
  output = vgg_model.get_layer('fc2').output
  output = Flatten(name='new_flatten')(output)
  output = Dense(units=1024, activation='relu', name='new_fc',kernel_initializer= He_initializer)(output)
  output = Dense(units=8, activation='softmax',kernel_initializer= He_initializer)(output)
  vgg_model = Model(vgg_model.input, output)

  # Make last 4 layers trainable if lastFourTrainable == True
  if lastFourTrainable == True:
    vgg_model.get_layer('block5_conv3').trainable = True
    vgg_model.get_layer('fc1').trainable = True
    vgg_model.get_layer('fc2').trainable = True
    vgg_model.get_layer('new_fc').trainable = True

  
  # Check the index of the trainable layer in Resnet and assign it below with learning_rate=1e-6
  
  optimizers = [
     tf.keras.optimizers.Adam(learning_rate=1e-6),
     tf.keras.optimizers.Adam(learning_rate=1e-3)]

  optimizers_and_layers = [(optimizers[1], vgg_model.layers[-1]),(optimizers[1], vgg_model.layers[-2]),
                            (optimizers[0], vgg_model.layers[-4]), (optimizers[0], vgg_model.layers[-5]),
                            (optimizers[0], vgg_model.layers[-8])]
  optimizer = tfa.optimizers.MultiOptimizer(optimizers_and_layers)

  #model.compile(optimizer=optimizer,loss='categorical_crossentropy',metrics=['accuracy'])


  # Compile VGG-16 model
  vgg_model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
  vgg_model.summary()

  return vgg_model

vgg16_model = getVGG16Model()

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels.h5
553467904/553467096 [==============================] - 18s 0us/step
553476096/553467096 [==============================] - 18s 0us/step
Model: "model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_1 (InputLayer)        [(None, 224, 224, 3)]     0         
                                                                 
 block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      
                                                                 
 block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     
                                                                 
 block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         
                                                                 
 block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     
                                                                 
 block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    
                                                                 
 block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         
                                                                 
 block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    
                                                                 
 block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         
                                                                 
 block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   
                                                                 
 block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         
                                                                 
 block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         
                                                                 
 flatten (Flatten)           (None, 25088)             0         
                                                                 
 fc1 (Dense)                 (None, 4096)              102764544 
                                                                 
 fc2 (Dense)                 (None, 4096)              16781312  
                                                                 
 new_flatten (Flatten)       (None, 4096)              0         
                                                                 
 new_fc (Dense)              (None, 1024)              4195328   
                                                                 
 dense (Dense)               (None, 8)                 8200      
                                                                 
=================================================================
Total params: 138,464,072
Trainable params: 4,203,528
Non-trainable params: 134,260,544
_________________________________________________________________

es = EarlyStopping(monitor='val_accuracy', mode='max', patience=5,  restore_best_weights=True) 

vgg16_model.fit(X_train_dg.flow(X_train, y_train), batch_size=50, epochs=40, validation_data=(X_test_uncr, y_test_uncr), callbacks=[es])
#vgg16_model.fit(X_train, y_train, epochs=40, validation_data=(X_test_uncr, y_test_uncr), batch_size=50)#, callbacks=[es]) #validation_split=0.15,

Epoch 1/40
21/21 [==============================] - 22s 472ms/step - loss: 1.4028 - accuracy: 0.7769 - val_loss: 2.9219 - val_accuracy: 0.6379
Epoch 2/40
21/21 [==============================] - 4s 211ms/step - loss: 0.1712 - accuracy: 0.9735 - val_loss: 1.6712 - val_accuracy: 0.7716
Epoch 3/40
21/21 [==============================] - 4s 215ms/step - loss: 0.0714 - accuracy: 0.9844 - val_loss: 1.4721 - val_accuracy: 0.8190
Epoch 4/40
21/21 [==============================] - 4s 206ms/step - loss: 0.0047 - accuracy: 0.9969 - val_loss: 1.5241 - val_accuracy: 0.8147
Epoch 5/40
21/21 [==============================] - 4s 200ms/step - loss: 0.0142 - accuracy: 0.9953 - val_loss: 3.2734 - val_accuracy: 0.7845
Epoch 6/40
21/21 [==============================] - 4s 201ms/step - loss: 0.0087 - accuracy: 0.9938 - val_loss: 2.6213 - val_accuracy: 0.8190
Epoch 7/40
21/21 [==============================] - 4s 202ms/step - loss: 0.0122 - accuracy: 0.9953 - val_loss: 2.5630 - val_accuracy: 0.7974
Epoch 8/40
21/21 [==============================] - 4s 211ms/step - loss: 0.0016 - accuracy: 0.9984 - val_loss: 2.9993 - val_accuracy: 0.7888

<keras.callbacks.History at 0x7f63df824610>

Testing the accuracy of the model

y_pred_cr = vgg16_model.predict(X_test_cr)
y_pred_uncr = vgg16_model.predict(X_test_uncr)

_, acc = vgg16_model.evaluate(X_test_cr, y_test_cr, verbose=0)
print('> %.3f' % (acc * 100.0))

_, acc = vgg16_model.evaluate(X_test_uncr, y_test_uncr, verbose=0)
print('> %.3f' % (acc * 100.0))

y_pred_cr_val = np.argmax(y_pred_cr, axis=1)
y_test_cr_val = np.argmax(y_test_cr,axis = 1)
print("Cropped")
print(y_pred_cr_val)
print(y_test_cr_val)


y_pred_uncr_val = np.argmax(y_pred_uncr, axis=1)
y_test_uncr_val = np.argmax(y_test_uncr,axis = 1)
print("Uncropped")
print(y_pred_uncr_val)
print(y_test_uncr_val)

Cropped
[7 5 1 4 0 4 5 1 3 6 3 4 7 4 4 3 0 3 2 5 4 3 5 5 4 4 1 2 7 5 4 7 3 4 1 5 2
 5 5 1 2 1 7 4 6 3 2 2 0 1 5 1 2 4 3 5 5 3 4 6 7 1 5 7 5 1 0 6 0 5 6 4 7 3
 6 1 6 1 5 4 4 4 4 1 7 2 5 2 2 3 0 1 7 1 1 3 1 5 1 3 5 4 7 3 3 4 1 5 4 2 4
 0 3 1 7 5 3 1 5 1 5 4 1 3 0 3 1 5 3 3 7 3 1 4 4 3 3 1 4 4 5 4 3 1 3 5 5 4
 4 0 3 2 3 5 3 5 1 5 1 1 1 4 5 6 4 4 5 1 7 2 3 1 3 3 3 3 1 4 2 7 4 3 1 0 0
 7 3 0 2 3 4 4 2 2 3 1 1 2 5 4 1 2 5 1 1 2 1 4 1 1 2 5 6 2 3 3 1 4 7 1 5 1
 5 1 3 3 0 1 4 4 3 3]
[7 5 1 4 1 4 5 1 3 6 3 4 7 4 4 3 0 6 6 5 4 3 5 5 4 4 1 6 1 5 4 7 6 4 1 5 2
 5 5 1 2 1 7 4 6 3 2 6 0 1 5 1 2 4 3 5 5 3 4 6 1 1 5 5 5 1 0 6 6 5 6 4 7 3
 6 1 6 1 5 4 4 4 4 1 7 6 5 2 2 3 0 1 7 1 1 3 1 5 1 6 5 4 1 3 3 4 1 5 4 6 4
 0 3 1 7 5 3 1 5 1 5 4 1 3 0 3 1 5 3 3 7 3 1 4 4 6 3 1 4 4 5 4 3 1 3 5 5 4
 4 0 3 2 3 5 3 5 1 5 1 1 1 6 5 6 4 4 5 1 7 6 3 1 3 3 3 4 1 4 2 7 4 3 1 0 0
 1 3 0 0 3 4 4 2 2 4 1 1 2 5 4 1 2 5 1 1 2 1 4 1 1 2 5 6 2 3 3 1 4 2 1 5 1
 5 2 3 3 0 1 4 4 3 3]
Uncropped
[4 1 4 1 2 4 4 1 1 4 4 7 1 1 1 3 0 7 3 2 5 5 5 4 6 4 5 4 7 5 1 4 0 4 3 5 7
 5 1 2 0 0 3 6 7 5 3 3 1 5 6 3 5 3 1 1 3 4 1 1 6 3 0 3 5 5 7 1 1 1 7 2 1 6
 4 1 7 3 7 4 3 3 7 3 4 0 4 4 3 6 5 4 5 1 3 1 2 7 5 4 3 1 3 7 0 2 5 3 7 4 1
 1 4 5 1 3 4 5 3 0 2 4 2 5 3 0 4 5 4 5 3 0 4 2 2 1 4 3 5 4 0 6 0 3 6 5 0 3
 1 1 5 7 5 4 6 4 0 6 0 7 4 1 5 4 4 4 5 4 1 3 1 1 1 2 0 7 4 6 7 5 3 6 1 7 4
 5 6 6 4 7 6 1 6 3 3 7 5 5 5 6 3 4 4 7 4 7 5 1 4 7 5 1 5 3 7 4 3 1 3 4 4 5
 4 6 4 3 4 1 4 3 1 1]
[4 1 4 1 3 4 4 1 1 4 3 1 1 1 1 3 3 1 3 2 5 5 5 4 2 4 5 4 7 5 1 4 0 4 3 5 7
 5 2 2 6 6 3 6 1 5 3 3 1 5 6 6 5 3 1 1 3 4 1 1 6 3 0 3 5 5 1 1 1 1 1 2 2 6
 4 1 7 3 7 4 3 3 1 3 4 0 4 4 7 6 5 4 5 1 3 1 5 1 5 4 3 1 3 1 0 2 5 3 1 4 1
 1 0 5 2 3 4 5 3 0 2 4 2 5 2 0 4 5 4 5 3 0 4 2 2 1 4 3 5 0 7 6 0 3 6 5 0 3
 1 1 5 1 5 4 6 4 6 6 3 7 4 1 5 4 4 4 5 4 1 3 1 1 1 2 7 7 4 6 7 5 7 6 2 1 4
 5 6 6 3 1 2 1 6 3 3 1 5 5 5 6 3 4 4 1 4 5 5 1 4 2 5 1 5 3 1 4 3 1 3 0 4 5
 4 6 6 3 4 1 4 3 1 1]

# Creating a dataframe for a array-formatted Confusion matrix,so it will be easy for plotting.
cm = confusion_matrix(y_test_cr_val, y_pred_cr_val)
cm_df = pd.DataFrame(cm,
                     index = ['1040', '1019', '1015', '1004', '1005', '1039','1017', '1021'], #,'1008', '1018'], #'1040':0, '1019':1, '1015':2, '1004':3, '1005':4, '1021'
                     columns = ['1040', '1019', '1015', '1004', '1005','1039','1017','1021']) #,'1008', '1018'])
                     #index = ['Low','Medium','High'], 
                     #columns = ['Low','Medium','High'])

#Plotting the confusion matrix
plt.figure(figsize=(7,6))
sns.heatmap(cm_df, annot=True)
plt.title('Confusion Matrix')
plt.ylabel('Actal Values')
plt.xlabel('Predicted Values')
plt.show()

# Creating a dataframe for a array-formatted Confusion matrix,so it will be easy for plotting.
cm = confusion_matrix(y_test_uncr_val, y_pred_uncr_val)
cm_df = pd.DataFrame(cm,
                     index = ['1040', '1019', '1015', '1004', '1005', '1039','1017','1021'],#, '1021'], #'1040':0, '1019':1, '1015':2, '1004':3, '1005':4, '1021'
                     columns = ['1040', '1019', '1015', '1004', '1005', '1039','1017','1021'])#,'1021'])
                     #index = ['Low','Medium','High'], 
                     #columns = ['Low','Medium','High'])

#Plotting the confusion matrix
plt.figure(figsize=(7,6))
sns.heatmap(cm_df, annot=True)
plt.title('Confusion Matrix')
plt.ylabel('Actal Values')
plt.xlabel('Predicted Values')
plt.show()

Last four Convolution layers in VGG16

X_train, y_train = preprocesing(img_train,classes)
X_test_cr, y_test_cr = preprocesing(img_test_cr,classes)
X_test_uncr, y_test_uncr = preprocesing(img_test_uncr,classes)

The shape of the dataset: (641, 224, 224, 3)
The shape of the label dataset: (641, 8)
The shape of the dataset: (232, 224, 224, 3)
The shape of the label dataset: (232, 8)
The shape of the dataset: (232, 224, 224, 3)
The shape of the label dataset: (232, 8)

def get_activations_at(model,input_image, i):
  #index the layer 
  out_layer = model.layers[i]
  
  #change the output of the model 
  model = tf.keras.models.Model(inputs = model.inputs, outputs = out_layer.output)
  
  #return the activations
  return model.predict(input_image)

def postprocess_activations(activations):

  #using the approach in https://arxiv.org/abs/1612.03928
  output = np.abs(activations)
  output = np.sum(output, axis = -1).squeeze()

  #resize and convert to image 
  output = cv2.resize(output, (224, 224))
  # output = output.reshape((256,256,3))
  output /= output.max()
  output *= 255
  return 255 - output.astype('uint8')

def apply_heatmap(weights, img):
  #generate heat maps 
  heatmap = cv2.applyColorMap(weights, cv2.COLORMAP_JET)
  heatmap = cv2.addWeighted(heatmap, 0.7, img, 0.3, 0)
  return heatmap

def plot_heatmaps(model,test_image,rng):
  level_maps = None

  # rng = range(7,14)

  #given a range of indices generate the heat maps 
  for i in rng:
    activations = get_activations_at(model,array, i)
    weights = postprocess_activations(activations)
    heatmap_img = cv2.applyColorMap(weights, cv2.COLORMAP_JET)
    fin = cv2.addWeighted(heatmap_img, 0.6, test_image, 0.4, 0)
    
    if level_maps is None:
      level_maps = fin
    else:
      level_maps = np.concatenate([level_maps, fin], axis = 1)
  plt.figure(figsize=(10, 10))
  plt.axis('off')
  ax = plt.imshow(level_maps)
  plt.show()

array.shape

(1, 224, 224, 3)

for i in range(10):
  # before preprocess input 
  test_image = X_test_uncr[0+i,:,:,:]
  #array = keras.preprocessing.image.img_to_array(test_image)
  
  array = np.expand_dims(test_image, axis=0)
  # plt.figure(figsize=(10, 10))
  print("Test Image",i+1)
  plt.imshow(test_image)
  print("Predicted Class Label",np.argmax(vgg16_model.predict(array)))
  plot_heatmaps(vgg16_model,test_image,range(16, 19))
  print()

Test Image 1
Predicted Class Label 3
WARNING:tensorflow:5 out of the last 37 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f634c518ef0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:6 out of the last 38 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f634c14fdd0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.

Test Image 2
Predicted Class Label 1

Test Image 3
Predicted Class Label 3

Test Image 4
Predicted Class Label 1

Test Image 5
Predicted Class Label 3

Test Image 6
Predicted Class Label 4

Test Image 7
Predicted Class Label 4

Test Image 8
Predicted Class Label 1

Test Image 9
Predicted Class Label 1

Test Image 10
Predicted Class Label 4

Grad cam and Guided Grad cam

x = X_test_uncr[40]
preprocessed_input = np.expand_dims(x, axis=0)
img = X_test_uncr[40]

plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
plt.axis('off')
plt.show()

layer_name = 'block5_conv3'
'''create a model till  last convolutional layers to have the best compromise between high-level semantics and detailed spatial
information'''
gb_model = tf.keras.models.Model(
    inputs = [vgg16_model.inputs],    
    outputs = [vgg16_model.get_layer(layer_name).output]
)
layer_dict = [layer for layer in gb_model.layers[1:] if hasattr(layer,'activation')]
layer_dict

[<keras.layers.convolutional.Conv2D at 0x7fae357c59d0>,
 <keras.layers.convolutional.Conv2D at 0x7fae3576ab90>,
 <keras.layers.convolutional.Conv2D at 0x7fadb86eb990>,
 <keras.layers.convolutional.Conv2D at 0x7fadb86ee690>,
 <keras.layers.convolutional.Conv2D at 0x7fadb86fa250>,
 <keras.layers.convolutional.Conv2D at 0x7fadb86ffa90>,
 <keras.layers.convolutional.Conv2D at 0x7fadb86f2910>,
 <keras.layers.convolutional.Conv2D at 0x7fadb870fa10>,
 <keras.layers.convolutional.Conv2D at 0x7fadb870bf90>,
 <keras.layers.convolutional.Conv2D at 0x7fadb8696890>,
 <keras.layers.convolutional.Conv2D at 0x7fadb869c810>,
 <keras.layers.convolutional.Conv2D at 0x7fadb8696110>,
 <keras.layers.convolutional.Conv2D at 0x7fadb86ac310>]

@tf.custom_gradient
def guidedRelu(x):
  def grad(dy):
    return tf.cast(dy>0,"float32") * tf.cast(x>0, "float32") * dy
  return tf.nn.relu(x), grad

for layer in layer_dict:
  if layer.activation == tf.keras.activations.relu:
    layer.activation = guidedRelu

with tf.GradientTape() as tape:
 inputs = tf.cast(preprocessed_input, tf.float32)
 tape.watch(inputs)
 outputs = gb_model(inputs)[0]
grads = tape.gradient(outputs,inputs)[0]

weights = tf.reduce_mean(grads, axis=(0, 1))
grad_cam = np.ones(outputs.shape[0: 2], dtype = np.float32)
for i, w in enumerate(weights):
    grad_cam += w * outputs[:, :, i]

grad_cam_img = cv2.resize(grad_cam.numpy(), (224, 224))
grad_cam_img = np.maximum(grad_cam_img, 0)
heatmap = (grad_cam_img - grad_cam_img.min()) / (grad_cam_img.max() - grad_cam_img.min())
grad_cam_img = cv2.applyColorMap(np.uint8(255*heatmap), cv2.COLORMAP_JET)
output_image = cv2.addWeighted(cv2.cvtColor(img.astype('uint8'), cv2.COLOR_RGB2BGR), 0.5, grad_cam_img, 1, 0)

plt.imshow(output_image)
plt.axis("off");
plt.show()

#Visualizing the guided back prop
guided_back_prop =grads
gb_viz = np.dstack((
            guided_back_prop[:, :, 0],
            guided_back_prop[:, :, 1],
            guided_back_prop[:, :, 2],
        ))       
gb_viz -= np.min(gb_viz)
gb_viz /= gb_viz.max()
    
imgplot = plt.imshow(gb_viz)
plt.axis("off")
plt.show()

from skimage.transform import resize
guided_cam = np.maximum(grad_cam, 0)
guided_cam = guided_cam / np.max(guided_cam) # scale 0 to 1.0
guided_cam = resize(guided_cam, (224,224), preserve_range=True)
#pointwise multiplcation of guided backprop and grad CAM 
gd_gb = np.dstack((
        guided_back_prop[:, :, 0] * guided_cam,
        guided_back_prop[:, :, 1] * guided_cam,
        guided_back_prop[:, :, 2] * guided_cam,
    ))
imgplot = plt.imshow(gd_gb)
plt.axis("off")
plt.show()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

VGG 19 new code

from keras.applications.vgg19 import VGG19, preprocess_input
from keras.layers import Flatten, Dense, GlobalMaxPooling2D
from tensorflow.keras.optimizers import Adam

from keras.applications.vgg19 import preprocess_input

X_train = preprocess_input(X_train)
X_test_cr = preprocess_input(X_test_cr)
X_test_uncr = preprocess_input(X_test_uncr)

X_train_dg = ImageDataGenerator()
X_train_dg.fit(X_train)

He_initializer = tf.keras.initializers.HeNormal()
def getVGG19Model(lastFourTrainable=False):
  #vgg_model = VGG16(weights='imagenet', input_shape=X_train[0].shape, include_top=True)
  vgg_model = VGG19(weights='imagenet', input_shape=X_train[0].shape, include_top=True)

  # Make all layers untrainable
  for layer in vgg_model.layers[:]:
      layer.trainable = False

  # Add fully connected layer which have 1024 neuron to VGG-16 model
  output = vgg_model.get_layer('fc2').output
  output = Flatten(name='new_flatten')(output)
  output = Dense(units=1024, activation='relu', name='new_fc',kernel_initializer= He_initializer)(output)
  output = Dense(units=8, activation='softmax',kernel_initializer= He_initializer)(output)
  vgg_model = Model(vgg_model.input, output)

  # Make last 4 layers trainable if lastFourTrainable == True
  if lastFourTrainable == True:
    vgg_model.get_layer('block5_conv4').trainable = True
    vgg_model.get_layer('fc1').trainable = True
    vgg_model.get_layer('fc2').trainable = True
    vgg_model.get_layer('new_fc').trainable = True
  
  optimizers = [
     tf.keras.optimizers.Adam(learning_rate=1e-6),
     tf.keras.optimizers.Adam(learning_rate=1e-3)]

  optimizers_and_layers = [(optimizers[1], vgg_model.layers[-1]),(optimizers[1], vgg_model.layers[-2]),
                            (optimizers[0], vgg_model.layers[-4]), (optimizers[0], vgg_model.layers[-5]),
                            (optimizers[0], vgg_model.layers[-8])]
  optimizer = tfa.optimizers.MultiOptimizer(optimizers_and_layers)

  


  # Compile VGG-16 model
  vgg_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
  vgg_model.summary()

  return vgg_model

vgg19_model = getVGG19Model()
#vgg19_model = getVGG19Model()

Model: "model_15"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_20 (InputLayer)       [(None, 224, 224, 3)]     0         
                                                                 
 block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      
                                                                 
 block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     
                                                                 
 block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         
                                                                 
 block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     
                                                                 
 block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    
                                                                 
 block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         
                                                                 
 block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    
                                                                 
 block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_conv4 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         
                                                                 
 block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   
                                                                 
 block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_conv4 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         
                                                                 
 block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv4 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         
                                                                 
 flatten (Flatten)           (None, 25088)             0         
                                                                 
 fc1 (Dense)                 (None, 4096)              102764544 
                                                                 
 fc2 (Dense)                 (None, 4096)              16781312  
                                                                 
 new_flatten (Flatten)       (None, 4096)              0         
                                                                 
 new_fc (Dense)              (None, 1024)              4195328   
                                                                 
 dense_15 (Dense)            (None, 8)                 8200      
                                                                 
=================================================================
Total params: 143,773,768
Trainable params: 4,203,528
Non-trainable params: 139,570,240
_________________________________________________________________

es = EarlyStopping(monitor='val_accuracy', mode='max', patience=5,  restore_best_weights=True) 

vgg19_model.fit(X_train_dg.flow(X_train, y_train), batch_size=50, epochs=40, validation_data=(X_test_uncr, y_test_uncr), callbacks=[es])
#vgg16_model.fit(X_train, y_train, epochs=40, validation_data=(X_test_uncr, y_test_uncr), batch_size=50)#, callbacks=[es]) #validation_split=0.15,

Epoch 1/40
21/21 [==============================] - 7s 293ms/step - loss: 8.7018 - accuracy: 0.5757 - val_loss: 2.0818 - val_accuracy: 0.7112
Epoch 2/40
21/21 [==============================] - 5s 249ms/step - loss: 0.4100 - accuracy: 0.9142 - val_loss: 1.9067 - val_accuracy: 0.6897
Epoch 3/40
21/21 [==============================] - 6s 266ms/step - loss: 0.0385 - accuracy: 0.9844 - val_loss: 1.5023 - val_accuracy: 0.7802
Epoch 4/40
21/21 [==============================] - 6s 266ms/step - loss: 0.0163 - accuracy: 0.9953 - val_loss: 1.4282 - val_accuracy: 0.7845
Epoch 5/40
21/21 [==============================] - 6s 270ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 1.3988 - val_accuracy: 0.7974
Epoch 6/40
21/21 [==============================] - 5s 256ms/step - loss: 3.5910e-04 - accuracy: 1.0000 - val_loss: 1.3790 - val_accuracy: 0.7974
Epoch 7/40
21/21 [==============================] - 5s 258ms/step - loss: 2.3583e-04 - accuracy: 1.0000 - val_loss: 1.3712 - val_accuracy: 0.7974
Epoch 8/40
21/21 [==============================] - 5s 259ms/step - loss: 1.9956e-04 - accuracy: 1.0000 - val_loss: 1.3636 - val_accuracy: 0.7888
Epoch 9/40
21/21 [==============================] - 5s 260ms/step - loss: 1.7112e-04 - accuracy: 1.0000 - val_loss: 1.3620 - val_accuracy: 0.7888
Epoch 10/40
21/21 [==============================] - 5s 263ms/step - loss: 1.5467e-04 - accuracy: 1.0000 - val_loss: 1.3581 - val_accuracy: 0.7888

<keras.callbacks.History at 0x7f4b2703c190>

y_pred_cr = vgg19_model.predict(X_test_cr)
y_pred_uncr = vgg19_model.predict(X_test_uncr)

_, acc = vgg19_model.evaluate(X_test_cr, y_test_cr, verbose=0)
print('> %.3f' % (acc * 100.0))

_, acc = vgg19_model.evaluate(X_test_uncr, y_test_uncr, verbose=0)
print('> %.3f' % (acc * 100.0))

> 85.776
> 79.741

y_pred_cr_val = np.argmax(y_pred_cr, axis=1)
y_test_cr_val = np.argmax(y_test_cr,axis = 1)
print("Cropped")
print(y_pred_cr_val)
print(y_test_cr_val)


y_pred_uncr_val = np.argmax(y_pred_uncr, axis=1)
y_test_uncr_val = np.argmax(y_test_uncr,axis = 1)
print("Uncropped")
print(y_pred_uncr_val)
print(y_test_uncr_val)

# Creating a dataframe for a array-formatted Confusion matrix,so it will be easy for plotting.
cm = confusion_matrix(y_test_cr_val, y_pred_cr_val)
cm_df = pd.DataFrame(cm,
                     index = ['1040', '1019', '1015', '1004', '1005', '1039','1017'],#, '1021'], #'1040':0, '1019':1, '1015':2, '1004':3, '1005':4, '1021'
                     columns = ['1040', '1019', '1015', '1004', '1005','1039','1017'])#,'1021'])
                     #index = ['Low','Medium','High'], 
                     #columns = ['Low','Medium','High'])

#Plotting the confusion matrix
plt.figure(figsize=(7,6))
sns.heatmap(cm_df, annot=True)
plt.title('Confusion Matrix')
plt.ylabel('Actal Values')
plt.xlabel('Predicted Values')
plt.show()

EFFICIENT NET

#from keras.applications.EfficientNetB0 import VGG19, preprocess_input
from keras.layers import Flatten, Dense, GlobalMaxPooling2D
from tensorflow.keras.optimizers import Adam

from keras.applications.efficientnet import EfficientNetB0, preprocess_input
X_train = preprocess_input(X_train)
X_test_cr = preprocess_input(X_test_cr)
X_test_uncr = preprocess_input(X_test_uncr)

X_train_dg = ImageDataGenerator()
X_train_dg.fit(X_train)

He_initializer = tf.keras.initializers.HeNormal()
from tensorflow.keras.applications import EfficientNetB0
from tensorflow.keras import layers, Model, Input

def getEfficientNetModel(lastFourTrainable=False):
  #vgg_model = VGG16(weights='imagenet', input_shape=X_train[0].shape, include_top=True)
  #vgg_model = VGG19(weights='imagenet', input_shape=X_train[0].shape, include_top=True)
  
  width_shape = 224
  height_shape = 224
  image_input = Input(shape=(width_shape, height_shape, 3))
  eff_model = EfficientNetB0(include_top=False, input_tensor=image_input, weights="imagenet")

  # Make all layers untrainable
  for layer in eff_model.layers[:]:
      layer.trainable = False

  # Add fully connected layer which have 1024 neuron to VGG-16 model
  output = eff_model.get_layer('top_activation').output
  output = Flatten(name='new_flatten')(output)
  output = Dense(units=1024, activation='relu', name='new_fc',kernel_initializer= He_initializer)(output)
  output = Dense(units=8, activation='softmax',kernel_initializer= He_initializer)(output)
  eff_model = Model(eff_model.input, output)

  #Make last 4 layers trainable if lastFourTrainable == True
  if lastFourTrainable == True:
    eff_model.get_layer('top_conv').trainable = True
    #eff_model.get_layer('block7a_project_conv').trainable = True
    
    #vgg_model.get_layer('fc1').trainable = True
    #vgg_model.get_layer('fc2').trainable = True
    eff_model.get_layer('new_fc').trainable = True
    
  # to run discriminative learning uncomment below
  # optimizers = [
  #    tf.keras.optimizers.Adam(learning_rate=1e-6),
  #    tf.keras.optimizers.Adam(learning_rate=1e-3)]

  # optimizers_and_layers = [(optimizers[1], eff_model.layers[-1]),(optimizers[1], eff_model.layers[-2]),
  #                           (optimizers[0], eff_model.layers[-6])]#, (optimizers[0], eff_model.layers[-8])]

  # optimizer = tfa.optimizers.MultiOptimizer(optimizers_and_layers)

  # eff_model.compile(optimizer=optimizer,loss='categorical_crossentropy',metrics=['accuracy'])


  # Compile VGG-16 model
  #eff_model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
  eff_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
  eff_model.summary()
  return eff_model

eff_model =  getEfficientNetModel()
#vgg19_model = getVGG19Model()

Model: "model_10"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_15 (InputLayer)          [(None, 224, 224, 3  0           []                               
                                )]                                                                
                                                                                                  
 rescaling_13 (Rescaling)       (None, 224, 224, 3)  0           ['input_15[0][0]']               
                                                                                                  
 normalization_13 (Normalizatio  (None, 224, 224, 3)  7          ['rescaling_13[0][0]']           
 n)                                                                                               
                                                                                                  
 stem_conv_pad (ZeroPadding2D)  (None, 225, 225, 3)  0           ['normalization_13[0][0]']       
                                                                                                  
 stem_conv (Conv2D)             (None, 112, 112, 32  864         ['stem_conv_pad[0][0]']          
                                )                                                                 
                                                                                                  
 stem_bn (BatchNormalization)   (None, 112, 112, 32  128         ['stem_conv[0][0]']              
                                )                                                                 
                                                                                                  
 stem_activation (Activation)   (None, 112, 112, 32  0           ['stem_bn[0][0]']                
                                )                                                                 
                                                                                                  
 block1a_dwconv (DepthwiseConv2  (None, 112, 112, 32  288        ['stem_activation[0][0]']        
 D)                             )                                                                 
                                                                                                  
 block1a_bn (BatchNormalization  (None, 112, 112, 32  128        ['block1a_dwconv[0][0]']         
 )                              )                                                                 
                                                                                                  
 block1a_activation (Activation  (None, 112, 112, 32  0          ['block1a_bn[0][0]']             
 )                              )                                                                 
                                                                                                  
 block1a_se_squeeze (GlobalAver  (None, 32)          0           ['block1a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block1a_se_reshape (Reshape)   (None, 1, 1, 32)     0           ['block1a_se_squeeze[0][0]']     
                                                                                                  
 block1a_se_reduce (Conv2D)     (None, 1, 1, 8)      264         ['block1a_se_reshape[0][0]']     
                                                                                                  
 block1a_se_expand (Conv2D)     (None, 1, 1, 32)     288         ['block1a_se_reduce[0][0]']      
                                                                                                  
 block1a_se_excite (Multiply)   (None, 112, 112, 32  0           ['block1a_activation[0][0]',     
                                )                                 'block1a_se_expand[0][0]']      
                                                                                                  
 block1a_project_conv (Conv2D)  (None, 112, 112, 16  512         ['block1a_se_excite[0][0]']      
                                )                                                                 
                                                                                                  
 block1a_project_bn (BatchNorma  (None, 112, 112, 16  64         ['block1a_project_conv[0][0]']   
 lization)                      )                                                                 
                                                                                                  
 block2a_expand_conv (Conv2D)   (None, 112, 112, 96  1536        ['block1a_project_bn[0][0]']     
                                )                                                                 
                                                                                                  
 block2a_expand_bn (BatchNormal  (None, 112, 112, 96  384        ['block2a_expand_conv[0][0]']    
 ization)                       )                                                                 
                                                                                                  
 block2a_expand_activation (Act  (None, 112, 112, 96  0          ['block2a_expand_bn[0][0]']      
 ivation)                       )                                                                 
                                                                                                  
 block2a_dwconv_pad (ZeroPaddin  (None, 113, 113, 96  0          ['block2a_expand_activation[0][0]
 g2D)                           )                                ']                               
                                                                                                  
 block2a_dwconv (DepthwiseConv2  (None, 56, 56, 96)  864         ['block2a_dwconv_pad[0][0]']     
 D)                                                                                               
                                                                                                  
 block2a_bn (BatchNormalization  (None, 56, 56, 96)  384         ['block2a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block2a_activation (Activation  (None, 56, 56, 96)  0           ['block2a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block2a_se_squeeze (GlobalAver  (None, 96)          0           ['block2a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2a_se_reshape (Reshape)   (None, 1, 1, 96)     0           ['block2a_se_squeeze[0][0]']     
                                                                                                  
 block2a_se_reduce (Conv2D)     (None, 1, 1, 4)      388         ['block2a_se_reshape[0][0]']     
                                                                                                  
 block2a_se_expand (Conv2D)     (None, 1, 1, 96)     480         ['block2a_se_reduce[0][0]']      
                                                                                                  
 block2a_se_excite (Multiply)   (None, 56, 56, 96)   0           ['block2a_activation[0][0]',     
                                                                  'block2a_se_expand[0][0]']      
                                                                                                  
 block2a_project_conv (Conv2D)  (None, 56, 56, 24)   2304        ['block2a_se_excite[0][0]']      
                                                                                                  
 block2a_project_bn (BatchNorma  (None, 56, 56, 24)  96          ['block2a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block2b_expand_conv (Conv2D)   (None, 56, 56, 144)  3456        ['block2a_project_bn[0][0]']     
                                                                                                  
 block2b_expand_bn (BatchNormal  (None, 56, 56, 144)  576        ['block2b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block2b_expand_activation (Act  (None, 56, 56, 144)  0          ['block2b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block2b_dwconv (DepthwiseConv2  (None, 56, 56, 144)  1296       ['block2b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block2b_bn (BatchNormalization  (None, 56, 56, 144)  576        ['block2b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block2b_activation (Activation  (None, 56, 56, 144)  0          ['block2b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block2b_se_squeeze (GlobalAver  (None, 144)         0           ['block2b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block2b_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2b_se_squeeze[0][0]']     
                                                                                                  
 block2b_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2b_se_reshape[0][0]']     
                                                                                                  
 block2b_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2b_se_reduce[0][0]']      
                                                                                                  
 block2b_se_excite (Multiply)   (None, 56, 56, 144)  0           ['block2b_activation[0][0]',     
                                                                  'block2b_se_expand[0][0]']      
                                                                                                  
 block2b_project_conv (Conv2D)  (None, 56, 56, 24)   3456        ['block2b_se_excite[0][0]']      
                                                                                                  
 block2b_project_bn (BatchNorma  (None, 56, 56, 24)  96          ['block2b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block2b_drop (Dropout)         (None, 56, 56, 24)   0           ['block2b_project_bn[0][0]']     
                                                                                                  
 block2b_add (Add)              (None, 56, 56, 24)   0           ['block2b_drop[0][0]',           
                                                                  'block2a_project_bn[0][0]']     
                                                                                                  
 block3a_expand_conv (Conv2D)   (None, 56, 56, 144)  3456        ['block2b_add[0][0]']            
                                                                                                  
 block3a_expand_bn (BatchNormal  (None, 56, 56, 144)  576        ['block3a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3a_expand_activation (Act  (None, 56, 56, 144)  0          ['block3a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3a_dwconv_pad (ZeroPaddin  (None, 59, 59, 144)  0          ['block3a_expand_activation[0][0]
 g2D)                                                            ']                               
                                                                                                  
 block3a_dwconv (DepthwiseConv2  (None, 28, 28, 144)  3600       ['block3a_dwconv_pad[0][0]']     
 D)                                                                                               
                                                                                                  
 block3a_bn (BatchNormalization  (None, 28, 28, 144)  576        ['block3a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3a_activation (Activation  (None, 28, 28, 144)  0          ['block3a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3a_se_squeeze (GlobalAver  (None, 144)         0           ['block3a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3a_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block3a_se_squeeze[0][0]']     
                                                                                                  
 block3a_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block3a_se_reshape[0][0]']     
                                                                                                  
 block3a_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block3a_se_reduce[0][0]']      
                                                                                                  
 block3a_se_excite (Multiply)   (None, 28, 28, 144)  0           ['block3a_activation[0][0]',     
                                                                  'block3a_se_expand[0][0]']      
                                                                                                  
 block3a_project_conv (Conv2D)  (None, 28, 28, 40)   5760        ['block3a_se_excite[0][0]']      
                                                                                                  
 block3a_project_bn (BatchNorma  (None, 28, 28, 40)  160         ['block3a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3b_expand_conv (Conv2D)   (None, 28, 28, 240)  9600        ['block3a_project_bn[0][0]']     
                                                                                                  
 block3b_expand_bn (BatchNormal  (None, 28, 28, 240)  960        ['block3b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block3b_expand_activation (Act  (None, 28, 28, 240)  0          ['block3b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block3b_dwconv (DepthwiseConv2  (None, 28, 28, 240)  6000       ['block3b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block3b_bn (BatchNormalization  (None, 28, 28, 240)  960        ['block3b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block3b_activation (Activation  (None, 28, 28, 240)  0          ['block3b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block3b_se_squeeze (GlobalAver  (None, 240)         0           ['block3b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block3b_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block3b_se_squeeze[0][0]']     
                                                                                                  
 block3b_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block3b_se_reshape[0][0]']     
                                                                                                  
 block3b_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block3b_se_reduce[0][0]']      
                                                                                                  
 block3b_se_excite (Multiply)   (None, 28, 28, 240)  0           ['block3b_activation[0][0]',     
                                                                  'block3b_se_expand[0][0]']      
                                                                                                  
 block3b_project_conv (Conv2D)  (None, 28, 28, 40)   9600        ['block3b_se_excite[0][0]']      
                                                                                                  
 block3b_project_bn (BatchNorma  (None, 28, 28, 40)  160         ['block3b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block3b_drop (Dropout)         (None, 28, 28, 40)   0           ['block3b_project_bn[0][0]']     
                                                                                                  
 block3b_add (Add)              (None, 28, 28, 40)   0           ['block3b_drop[0][0]',           
                                                                  'block3a_project_bn[0][0]']     
                                                                                                  
 block4a_expand_conv (Conv2D)   (None, 28, 28, 240)  9600        ['block3b_add[0][0]']            
                                                                                                  
 block4a_expand_bn (BatchNormal  (None, 28, 28, 240)  960        ['block4a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4a_expand_activation (Act  (None, 28, 28, 240)  0          ['block4a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4a_dwconv_pad (ZeroPaddin  (None, 29, 29, 240)  0          ['block4a_expand_activation[0][0]
 g2D)                                                            ']                               
                                                                                                  
 block4a_dwconv (DepthwiseConv2  (None, 14, 14, 240)  2160       ['block4a_dwconv_pad[0][0]']     
 D)                                                                                               
                                                                                                  
 block4a_bn (BatchNormalization  (None, 14, 14, 240)  960        ['block4a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4a_activation (Activation  (None, 14, 14, 240)  0          ['block4a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4a_se_squeeze (GlobalAver  (None, 240)         0           ['block4a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4a_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block4a_se_squeeze[0][0]']     
                                                                                                  
 block4a_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block4a_se_reshape[0][0]']     
                                                                                                  
 block4a_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block4a_se_reduce[0][0]']      
                                                                                                  
 block4a_se_excite (Multiply)   (None, 14, 14, 240)  0           ['block4a_activation[0][0]',     
                                                                  'block4a_se_expand[0][0]']      
                                                                                                  
 block4a_project_conv (Conv2D)  (None, 14, 14, 80)   19200       ['block4a_se_excite[0][0]']      
                                                                                                  
 block4a_project_bn (BatchNorma  (None, 14, 14, 80)  320         ['block4a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4b_expand_conv (Conv2D)   (None, 14, 14, 480)  38400       ['block4a_project_bn[0][0]']     
                                                                                                  
 block4b_expand_bn (BatchNormal  (None, 14, 14, 480)  1920       ['block4b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4b_expand_activation (Act  (None, 14, 14, 480)  0          ['block4b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4b_dwconv (DepthwiseConv2  (None, 14, 14, 480)  4320       ['block4b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4b_bn (BatchNormalization  (None, 14, 14, 480)  1920       ['block4b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4b_activation (Activation  (None, 14, 14, 480)  0          ['block4b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4b_se_squeeze (GlobalAver  (None, 480)         0           ['block4b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4b_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4b_se_squeeze[0][0]']     
                                                                                                  
 block4b_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4b_se_reshape[0][0]']     
                                                                                                  
 block4b_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4b_se_reduce[0][0]']      
                                                                                                  
 block4b_se_excite (Multiply)   (None, 14, 14, 480)  0           ['block4b_activation[0][0]',     
                                                                  'block4b_se_expand[0][0]']      
                                                                                                  
 block4b_project_conv (Conv2D)  (None, 14, 14, 80)   38400       ['block4b_se_excite[0][0]']      
                                                                                                  
 block4b_project_bn (BatchNorma  (None, 14, 14, 80)  320         ['block4b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4b_drop (Dropout)         (None, 14, 14, 80)   0           ['block4b_project_bn[0][0]']     
                                                                                                  
 block4b_add (Add)              (None, 14, 14, 80)   0           ['block4b_drop[0][0]',           
                                                                  'block4a_project_bn[0][0]']     
                                                                                                  
 block4c_expand_conv (Conv2D)   (None, 14, 14, 480)  38400       ['block4b_add[0][0]']            
                                                                                                  
 block4c_expand_bn (BatchNormal  (None, 14, 14, 480)  1920       ['block4c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block4c_expand_activation (Act  (None, 14, 14, 480)  0          ['block4c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block4c_dwconv (DepthwiseConv2  (None, 14, 14, 480)  4320       ['block4c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block4c_bn (BatchNormalization  (None, 14, 14, 480)  1920       ['block4c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block4c_activation (Activation  (None, 14, 14, 480)  0          ['block4c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block4c_se_squeeze (GlobalAver  (None, 480)         0           ['block4c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block4c_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4c_se_squeeze[0][0]']     
                                                                                                  
 block4c_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4c_se_reshape[0][0]']     
                                                                                                  
 block4c_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4c_se_reduce[0][0]']      
                                                                                                  
 block4c_se_excite (Multiply)   (None, 14, 14, 480)  0           ['block4c_activation[0][0]',     
                                                                  'block4c_se_expand[0][0]']      
                                                                                                  
 block4c_project_conv (Conv2D)  (None, 14, 14, 80)   38400       ['block4c_se_excite[0][0]']      
                                                                                                  
 block4c_project_bn (BatchNorma  (None, 14, 14, 80)  320         ['block4c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block4c_drop (Dropout)         (None, 14, 14, 80)   0           ['block4c_project_bn[0][0]']     
                                                                                                  
 block4c_add (Add)              (None, 14, 14, 80)   0           ['block4c_drop[0][0]',           
                                                                  'block4b_add[0][0]']            
                                                                                                  
 block5a_expand_conv (Conv2D)   (None, 14, 14, 480)  38400       ['block4c_add[0][0]']            
                                                                                                  
 block5a_expand_bn (BatchNormal  (None, 14, 14, 480)  1920       ['block5a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5a_expand_activation (Act  (None, 14, 14, 480)  0          ['block5a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5a_dwconv (DepthwiseConv2  (None, 14, 14, 480)  12000      ['block5a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5a_bn (BatchNormalization  (None, 14, 14, 480)  1920       ['block5a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5a_activation (Activation  (None, 14, 14, 480)  0          ['block5a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5a_se_squeeze (GlobalAver  (None, 480)         0           ['block5a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5a_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block5a_se_squeeze[0][0]']     
                                                                                                  
 block5a_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block5a_se_reshape[0][0]']     
                                                                                                  
 block5a_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block5a_se_reduce[0][0]']      
                                                                                                  
 block5a_se_excite (Multiply)   (None, 14, 14, 480)  0           ['block5a_activation[0][0]',     
                                                                  'block5a_se_expand[0][0]']      
                                                                                                  
 block5a_project_conv (Conv2D)  (None, 14, 14, 112)  53760       ['block5a_se_excite[0][0]']      
                                                                                                  
 block5a_project_bn (BatchNorma  (None, 14, 14, 112)  448        ['block5a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5b_expand_conv (Conv2D)   (None, 14, 14, 672)  75264       ['block5a_project_bn[0][0]']     
                                                                                                  
 block5b_expand_bn (BatchNormal  (None, 14, 14, 672)  2688       ['block5b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5b_expand_activation (Act  (None, 14, 14, 672)  0          ['block5b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5b_dwconv (DepthwiseConv2  (None, 14, 14, 672)  16800      ['block5b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5b_bn (BatchNormalization  (None, 14, 14, 672)  2688       ['block5b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5b_activation (Activation  (None, 14, 14, 672)  0          ['block5b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5b_se_squeeze (GlobalAver  (None, 672)         0           ['block5b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5b_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5b_se_squeeze[0][0]']     
                                                                                                  
 block5b_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5b_se_reshape[0][0]']     
                                                                                                  
 block5b_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5b_se_reduce[0][0]']      
                                                                                                  
 block5b_se_excite (Multiply)   (None, 14, 14, 672)  0           ['block5b_activation[0][0]',     
                                                                  'block5b_se_expand[0][0]']      
                                                                                                  
 block5b_project_conv (Conv2D)  (None, 14, 14, 112)  75264       ['block5b_se_excite[0][0]']      
                                                                                                  
 block5b_project_bn (BatchNorma  (None, 14, 14, 112)  448        ['block5b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5b_drop (Dropout)         (None, 14, 14, 112)  0           ['block5b_project_bn[0][0]']     
                                                                                                  
 block5b_add (Add)              (None, 14, 14, 112)  0           ['block5b_drop[0][0]',           
                                                                  'block5a_project_bn[0][0]']     
                                                                                                  
 block5c_expand_conv (Conv2D)   (None, 14, 14, 672)  75264       ['block5b_add[0][0]']            
                                                                                                  
 block5c_expand_bn (BatchNormal  (None, 14, 14, 672)  2688       ['block5c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block5c_expand_activation (Act  (None, 14, 14, 672)  0          ['block5c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block5c_dwconv (DepthwiseConv2  (None, 14, 14, 672)  16800      ['block5c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block5c_bn (BatchNormalization  (None, 14, 14, 672)  2688       ['block5c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block5c_activation (Activation  (None, 14, 14, 672)  0          ['block5c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block5c_se_squeeze (GlobalAver  (None, 672)         0           ['block5c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block5c_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5c_se_squeeze[0][0]']     
                                                                                                  
 block5c_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5c_se_reshape[0][0]']     
                                                                                                  
 block5c_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5c_se_reduce[0][0]']      
                                                                                                  
 block5c_se_excite (Multiply)   (None, 14, 14, 672)  0           ['block5c_activation[0][0]',     
                                                                  'block5c_se_expand[0][0]']      
                                                                                                  
 block5c_project_conv (Conv2D)  (None, 14, 14, 112)  75264       ['block5c_se_excite[0][0]']      
                                                                                                  
 block5c_project_bn (BatchNorma  (None, 14, 14, 112)  448        ['block5c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block5c_drop (Dropout)         (None, 14, 14, 112)  0           ['block5c_project_bn[0][0]']     
                                                                                                  
 block5c_add (Add)              (None, 14, 14, 112)  0           ['block5c_drop[0][0]',           
                                                                  'block5b_add[0][0]']            
                                                                                                  
 block6a_expand_conv (Conv2D)   (None, 14, 14, 672)  75264       ['block5c_add[0][0]']            
                                                                                                  
 block6a_expand_bn (BatchNormal  (None, 14, 14, 672)  2688       ['block6a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6a_expand_activation (Act  (None, 14, 14, 672)  0          ['block6a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6a_dwconv_pad (ZeroPaddin  (None, 17, 17, 672)  0          ['block6a_expand_activation[0][0]
 g2D)                                                            ']                               
                                                                                                  
 block6a_dwconv (DepthwiseConv2  (None, 7, 7, 672)   16800       ['block6a_dwconv_pad[0][0]']     
 D)                                                                                               
                                                                                                  
 block6a_bn (BatchNormalization  (None, 7, 7, 672)   2688        ['block6a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6a_activation (Activation  (None, 7, 7, 672)   0           ['block6a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6a_se_squeeze (GlobalAver  (None, 672)         0           ['block6a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6a_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block6a_se_squeeze[0][0]']     
                                                                                                  
 block6a_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block6a_se_reshape[0][0]']     
                                                                                                  
 block6a_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block6a_se_reduce[0][0]']      
                                                                                                  
 block6a_se_excite (Multiply)   (None, 7, 7, 672)    0           ['block6a_activation[0][0]',     
                                                                  'block6a_se_expand[0][0]']      
                                                                                                  
 block6a_project_conv (Conv2D)  (None, 7, 7, 192)    129024      ['block6a_se_excite[0][0]']      
                                                                                                  
 block6a_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6b_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6a_project_bn[0][0]']     
                                                                                                  
 block6b_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block6b_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6b_expand_activation (Act  (None, 7, 7, 1152)  0           ['block6b_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6b_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  28800       ['block6b_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6b_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block6b_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6b_activation (Activation  (None, 7, 7, 1152)  0           ['block6b_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6b_se_squeeze (GlobalAver  (None, 1152)        0           ['block6b_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6b_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6b_se_squeeze[0][0]']     
                                                                                                  
 block6b_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6b_se_reshape[0][0]']     
                                                                                                  
 block6b_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6b_se_reduce[0][0]']      
                                                                                                  
 block6b_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block6b_activation[0][0]',     
                                                                  'block6b_se_expand[0][0]']      
                                                                                                  
 block6b_project_conv (Conv2D)  (None, 7, 7, 192)    221184      ['block6b_se_excite[0][0]']      
                                                                                                  
 block6b_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6b_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6b_drop (Dropout)         (None, 7, 7, 192)    0           ['block6b_project_bn[0][0]']     
                                                                                                  
 block6b_add (Add)              (None, 7, 7, 192)    0           ['block6b_drop[0][0]',           
                                                                  'block6a_project_bn[0][0]']     
                                                                                                  
 block6c_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6b_add[0][0]']            
                                                                                                  
 block6c_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block6c_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6c_expand_activation (Act  (None, 7, 7, 1152)  0           ['block6c_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6c_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  28800       ['block6c_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6c_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block6c_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6c_activation (Activation  (None, 7, 7, 1152)  0           ['block6c_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6c_se_squeeze (GlobalAver  (None, 1152)        0           ['block6c_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6c_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6c_se_squeeze[0][0]']     
                                                                                                  
 block6c_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6c_se_reshape[0][0]']     
                                                                                                  
 block6c_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6c_se_reduce[0][0]']      
                                                                                                  
 block6c_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block6c_activation[0][0]',     
                                                                  'block6c_se_expand[0][0]']      
                                                                                                  
 block6c_project_conv (Conv2D)  (None, 7, 7, 192)    221184      ['block6c_se_excite[0][0]']      
                                                                                                  
 block6c_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6c_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6c_drop (Dropout)         (None, 7, 7, 192)    0           ['block6c_project_bn[0][0]']     
                                                                                                  
 block6c_add (Add)              (None, 7, 7, 192)    0           ['block6c_drop[0][0]',           
                                                                  'block6b_add[0][0]']            
                                                                                                  
 block6d_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6c_add[0][0]']            
                                                                                                  
 block6d_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block6d_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block6d_expand_activation (Act  (None, 7, 7, 1152)  0           ['block6d_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block6d_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  28800       ['block6d_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block6d_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block6d_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block6d_activation (Activation  (None, 7, 7, 1152)  0           ['block6d_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block6d_se_squeeze (GlobalAver  (None, 1152)        0           ['block6d_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block6d_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6d_se_squeeze[0][0]']     
                                                                                                  
 block6d_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6d_se_reshape[0][0]']     
                                                                                                  
 block6d_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6d_se_reduce[0][0]']      
                                                                                                  
 block6d_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block6d_activation[0][0]',     
                                                                  'block6d_se_expand[0][0]']      
                                                                                                  
 block6d_project_conv (Conv2D)  (None, 7, 7, 192)    221184      ['block6d_se_excite[0][0]']      
                                                                                                  
 block6d_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6d_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 block6d_drop (Dropout)         (None, 7, 7, 192)    0           ['block6d_project_bn[0][0]']     
                                                                                                  
 block6d_add (Add)              (None, 7, 7, 192)    0           ['block6d_drop[0][0]',           
                                                                  'block6c_add[0][0]']            
                                                                                                  
 block7a_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6d_add[0][0]']            
                                                                                                  
 block7a_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block7a_expand_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 block7a_expand_activation (Act  (None, 7, 7, 1152)  0           ['block7a_expand_bn[0][0]']      
 ivation)                                                                                         
                                                                                                  
 block7a_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  10368       ['block7a_expand_activation[0][0]
 D)                                                              ']                               
                                                                                                  
 block7a_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block7a_dwconv[0][0]']         
 )                                                                                                
                                                                                                  
 block7a_activation (Activation  (None, 7, 7, 1152)  0           ['block7a_bn[0][0]']             
 )                                                                                                
                                                                                                  
 block7a_se_squeeze (GlobalAver  (None, 1152)        0           ['block7a_activation[0][0]']     
 agePooling2D)                                                                                    
                                                                                                  
 block7a_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block7a_se_squeeze[0][0]']     
                                                                                                  
 block7a_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block7a_se_reshape[0][0]']     
                                                                                                  
 block7a_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block7a_se_reduce[0][0]']      
                                                                                                  
 block7a_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block7a_activation[0][0]',     
                                                                  'block7a_se_expand[0][0]']      
                                                                                                  
 block7a_project_conv (Conv2D)  (None, 7, 7, 320)    368640      ['block7a_se_excite[0][0]']      
                                                                                                  
 block7a_project_bn (BatchNorma  (None, 7, 7, 320)   1280        ['block7a_project_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 top_conv (Conv2D)              (None, 7, 7, 1280)   409600      ['block7a_project_bn[0][0]']     
                                                                                                  
 top_bn (BatchNormalization)    (None, 7, 7, 1280)   5120        ['top_conv[0][0]']               
                                                                                                  
 top_activation (Activation)    (None, 7, 7, 1280)   0           ['top_bn[0][0]']                 
                                                                                                  
 new_flatten (Flatten)          (None, 62720)        0           ['top_activation[0][0]']         
                                                                                                  
 new_fc (Dense)                 (None, 1024)         64226304    ['new_flatten[0][0]']            
                                                                                                  
 dense_10 (Dense)               (None, 8)            8200        ['new_fc[0][0]']                 
                                                                                                  
==================================================================================================
Total params: 68,284,075
Trainable params: 64,234,504
Non-trainable params: 4,049,571
__________________________________________________________________________________________________

es = EarlyStopping(monitor='val_accuracy', mode='max', patience=5,  restore_best_weights=True) 

#eff_model.fit(X_train_dg.flow(X_train, y_train), batch_size=50, epochs=25, validation_data=(X_test_uncr, y_test_uncr), callbacks=[es])
eff_model.fit(X_train, y_train, epochs=25, validation_data=(X_test_uncr, y_test_uncr), batch_size=50, callbacks=[es])#, callbacks=[es]) #validation_split=0.15,

Epoch 1/25
13/13 [==============================] - 2s 194ms/step - loss: 0.0061 - accuracy: 0.9984 - val_loss: 13.1741 - val_accuracy: 0.6207
Epoch 2/25
13/13 [==============================] - 2s 136ms/step - loss: 0.0027 - accuracy: 0.9984 - val_loss: 15.8946 - val_accuracy: 0.5388
Epoch 3/25
13/13 [==============================] - 2s 136ms/step - loss: 1.6255e-06 - accuracy: 1.0000 - val_loss: 19.7145 - val_accuracy: 0.4397
Epoch 4/25
13/13 [==============================] - 2s 137ms/step - loss: 0.0337 - accuracy: 0.9984 - val_loss: 13.0589 - val_accuracy: 0.4569
Epoch 5/25
13/13 [==============================] - 2s 136ms/step - loss: 6.3974e-08 - accuracy: 1.0000 - val_loss: 13.2274 - val_accuracy: 0.4871
Epoch 6/25
13/13 [==============================] - 2s 149ms/step - loss: 5.3563e-05 - accuracy: 1.0000 - val_loss: 13.3827 - val_accuracy: 0.4871

<keras.callbacks.History at 0x7f4b27497c50>

y_pred_cr = eff_model.predict(X_test_cr)
y_pred_uncr = eff_model.predict(X_test_uncr)

_, acc = eff_model.evaluate(X_test_cr, y_test_cr, verbose=0)
print('> %.3f' % (acc * 100.0))

_, acc = eff_model.evaluate(X_test_uncr, y_test_uncr, verbose=0)
print('> %.3f' % (acc * 100.0))

> 66.810
> 62.069

_, acc = eff_model.evaluate(X_test_cr, y_test_cr, verbose=0)
print('> %.3f' % (acc * 100.0))

_, acc = eff_model.evaluate(X_test_uncr, y_test_uncr, verbose=0)
print('> %.3f' % (acc * 100.0))

> 74.569
> 65.948